Social Networks with Rich Edge Semantics
Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negativ...
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| Κύριοι συγγραφείς: | , |
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| Μορφή: | Online |
| Γλώσσα: | Αγγλικά |
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Taylor & Francis
2025
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| Θέματα: | |
| Διαθέσιμο Online: | ONIX_20250422_9781315390611_20a |
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| _version_ | 1869516191686459392 |
|---|---|
| author | Zheng, Quan Skillicorn, David |
| author_browse | Skillicorn, David Zheng, Quan |
| author_facet | Zheng, Quan Skillicorn, David |
| author_sort | Zheng, Quan |
| collection | Directory of Open Access Books |
| description | Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets. Features Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks. |
| format | Online |
| id | doab-20.500.12854ir-158749 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Taylor & Francis |
| publisherStr | Taylor & Francis |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1587492025-07-29T17:46:00Z Social Networks with Rich Edge Semantics Zheng, Quan Skillicorn, David Area VIP SSL Approach Negative Relationships Diagonal Degree Matrix Spectral Embedding Negative Edge Weight Spectral Embedding Technique Unnormalized Laplacians Rayleigh Quotient Laplacian Normalizations Laplacian Matrix Random Walk Matrix Adjacency Matrix Normalized Edge Lengths Lazy Random Walk Von Luxburg Real World Dataset Edge Weight Negative Edges Transportation Networks Undirected Graph Positive Edges Original Social Network Vertical Edges Florentine Families thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets. Features Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks. 2025-04-23T06:57:18Z 2025-04-23T06:57:18Z 2025-04-22T11:38:38Z 2017 book ONIX_20250422_9781315390611_20a https://library.oapen.org/handle/20.500.12657/101026 9781315390611 9781315390604 9780367573256 9781315390628 9781138032439 9781315390598 https://directory.doabooks.org/handle/20.500.12854/158749 eng Chapman & Hall/CRC Data Mining and Knowledge Discovery Series open access image/jpeg image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International Attribution-NonCommercial-NoDerivatives 4.0 International https://library.oapen.org/bitstream/20.500.12657/101026/1/9781315390611.pdf https://library.oapen.org/bitstream/20.500.12657/101026/1/9781315390611.pdf Taylor & Francis CRC Press 10.1201/9781315390628 10.1201/9781315390628 fa69b019-f4ee-4979-8d42-c6b6c476b5f0 Knowledge Unlatched b818ba9d-2dd9-4fd7-a364-7f305aef7ee9 9781315390611 9781315390604 9780367573256 9781315390628 9781138032439 9781315390598 Knowledge Unlatched (KU) KU Select 2018: STEM Backlist Books CRC Press 230 [...] open access |
| spellingShingle | Area VIP SSL Approach Negative Relationships Diagonal Degree Matrix Spectral Embedding Negative Edge Weight Spectral Embedding Technique Unnormalized Laplacians Rayleigh Quotient Laplacian Normalizations Laplacian Matrix Random Walk Matrix Adjacency Matrix Normalized Edge Lengths Lazy Random Walk Von Luxburg Real World Dataset Edge Weight Negative Edges Transportation Networks Undirected Graph Positive Edges Original Social Network Vertical Edges Florentine Families thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science Zheng, Quan Skillicorn, David Social Networks with Rich Edge Semantics |
| title | Social Networks with Rich Edge Semantics |
| title_full | Social Networks with Rich Edge Semantics |
| title_fullStr | Social Networks with Rich Edge Semantics |
| title_full_unstemmed | Social Networks with Rich Edge Semantics |
| title_short | Social Networks with Rich Edge Semantics |
| title_sort | social networks with rich edge semantics |
| topic | Area VIP SSL Approach Negative Relationships Diagonal Degree Matrix Spectral Embedding Negative Edge Weight Spectral Embedding Technique Unnormalized Laplacians Rayleigh Quotient Laplacian Normalizations Laplacian Matrix Random Walk Matrix Adjacency Matrix Normalized Edge Lengths Lazy Random Walk Von Luxburg Real World Dataset Edge Weight Negative Edges Transportation Networks Undirected Graph Positive Edges Original Social Network Vertical Edges Florentine Families thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | Area VIP SSL Approach Negative Relationships Diagonal Degree Matrix Spectral Embedding Negative Edge Weight Spectral Embedding Technique Unnormalized Laplacians Rayleigh Quotient Laplacian Normalizations Laplacian Matrix Random Walk Matrix Adjacency Matrix Normalized Edge Lengths Lazy Random Walk Von Luxburg Real World Dataset Edge Weight Negative Edges Transportation Networks Undirected Graph Positive Edges Original Social Network Vertical Edges Florentine Families thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20250422_9781315390611_20a |
| work_keys_str_mv | AT zhengquan socialnetworkswithrichedgesemantics AT skillicorndavid socialnetworkswithrichedgesemantics |